ViSiElse: An innovative visualization R package to ensure behavioral raw data reliability and transparency - Garnier, Elodie Marie; Fouret, Nastasia; Descoins, Médéric (PeerJ Preprints, 2019)
Background. In recent years, the scientific community encouraged the use of raw data graphs to improve the reliability and transparency of the results presented in papers. However, methods to visualize raw data are limited to one variable per graph and/or only small samples. In behavioral science as in many other fields, multiple variables need to be plotted together to allow insights of the behavior or the process observations. In this paper, we present ViSiElse, an R-package offering a new approach in raw data visualization. Methods. ViSiElse was developed with the open-source software R to provide a solution for the complete visualization of the raw time data. Results. ViSiElse grants a global overview of a process by combining the visualization of multiple actions timestamps for all participants in a single graph. Individuals and/or group behavior can easily be assessed. Supplementary features allow users to further inspect their data by adding statistical indicators and/or time constraints. ViSiElse provides a global visualization of actions acquired from timestamps in any quantifiable observations.
Keywords: data visualization; raw data; behavior; data transparency; R package; graph; timestamps; actions